Sync person-detection from metro-analytics-catalog

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+expected_output_dlstreamer.gif filter=lfs diff=lfs merge=lfs -text
+expected_output_openvino.jpg filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,7 +1,5 @@
 # Person Detection
 
-> **Validated with:** OpenVINO 2026.1.0, NNCF 3.0.0, DLStreamer 2026.0, Ultralytics 8.4.46, Python 3.11+
-
 | Property | Value |
 |---|---|
 | **Category** | Object Detection (Person Detection) |
@@ -78,7 +76,7 @@ The second argument selects the precision (`FP32`, `FP16`, `INT8`); the default
 The script performs the following steps:
 
 1. Installs dependencies (`openvino`, `ultralytics`; adds `nncf` for INT8).
-2. Downloads a sample test image (`test.jpg`).
+2. Downloads a sample test image (`test.jpg`) and a sample test video (`test_video.mp4`).
 3. Downloads the PyTorch weights and exports to OpenVINO IR.
 4. *(INT8 only)* Quantizes the model using NNCF post-training quantization.
 
@@ -147,7 +145,7 @@ cv2.putText(
     image, f"Persons: {person_count}", (10, 30),
     cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 255, 0), 2,
 )
-cv2.imwrite("output.jpg", image)
+cv2.imwrite("output_openvino.jpg", image)
 ```
 
 **Device targets:**
@@ -160,7 +158,7 @@ cv2.imwrite("output.jpg", image)
 
 The `export_and_quantize.sh` script downloads `test.jpg` automatically.
 Re-run the OpenVINO sample above.
-The script reads `test.jpg`, prints the person count to the console, and writes the annotated frame to `output.jpg`.
+The script reads `test.jpg`, prints the person count to the console, and writes the annotated frame to `output_openvino.jpg`.
 
 Expected console output:
 
@@ -168,14 +166,19 @@ Expected console output:
 Detected persons: 2
 ```
 
-`output.jpg` shows a green bounding box around each detected person and the text `Persons: 2` in the top-left corner.
+`output_openvino.jpg` shows a green bounding box around each detected person and the text `Persons: 2` in the top-left corner.
+
+#### Expected Output
+
+![OpenVINO expected output](expected_output_openvino.jpg)
 
 ### DLStreamer Sample
 
-The pipeline below runs the FP16 YOLO26 detector on a single image via
+The pipeline below runs the FP16 YOLO26 detector on the sample video via
 `gvadetect`, filters detections to the `person` class in a buffer probe using
 the DLStreamer Python bindings (`gstgva.VideoFrame`), overlays bounding boxes,
-and prints the person count.
+saves the annotated result to `output_dlstreamer.mp4`, and prints the person count per
+frame.
 
 > **Notes on running this sample:**
 >
@@ -195,8 +198,6 @@ and prints the person count.
 >   /opt/intel/dlstreamer/gstreamer/lib/python3/dist-packages:${PYTHONPATH:-}
 >   ```
 
-**Image-based quick test** (uses `filesrc` with a single JPEG):
-
 ```python
 import gi
 
@@ -207,16 +208,19 @@ from gstgva import VideoFrame
 
 Gst.init(None)
 
-# For GPU: change device=CPU to device=GPU, add vapostproc !
-# video/x-raw(memory:VASurface) after jpegdec, and set
-# pre-process-backend=vaapi-surface-sharing on gvadetect.
-# For NPU: change device=CPU to device=NPU (batch-size=1 recommended).
+INPUT_VIDEO = "test_video.mp4"
+
+# For CPU: change device=GPU to device=CPU.
+# For NPU: change device=GPU to device=NPU (batch-size=1, nireq=4 recommended).
 pipeline_str = (
-    "filesrc location=test.jpg ! jpegdec ! videoconvert ! "
-    "video/x-raw,format=BGR ! "
+    f"filesrc location={INPUT_VIDEO} ! decodebin3 ! "
+    "videoconvert ! "
     "gvadetect model=yolo26n_openvino_model/yolo26n.xml "
-    "device=CPU threshold=0.4 ! queue ! "
-    "gvawatermark ! videoconvert ! jpegenc ! filesink name=sink location=output.jpg"
+    "device=GPU "
+    "threshold=0.4 ! queue ! "
+    "gvawatermark ! videoconvert ! video/x-raw,format=I420 ! "
+    "openh264enc ! h264parse ! "
+    "mp4mux ! filesink name=sink location=output_dlstreamer.mp4"
 )
 pipeline = Gst.parse_launch(pipeline_str)
 
@@ -226,7 +230,8 @@ def on_buffer(pad, info):
     caps = pad.get_current_caps()
     frame = VideoFrame(buf, caps=caps)
     person_count = sum(1 for r in frame.regions() if r.label() == "person")
-    print(f"Person count: {person_count}", flush=True)
+    if person_count:
+        print(f"Person count: {person_count}", flush=True)
     return Gst.PadProbeReturn.OK
 
 
@@ -243,11 +248,15 @@ bus.timed_pop_filtered(
 pipeline.set_state(Gst.State.NULL)
 ```
 
+#### Expected Output
+
+![DLStreamer expected output](expected_output_dlstreamer.gif)
+
 **Device targets:**
 
-- `device=CPU` -- default in the sample code.
-- `device=GPU` -- add `vapostproc ! video/x-raw(memory:VASurface)` after `jpegdec` and set `pre-process-backend=vaapi-surface-sharing` on `gvadetect`.
-- `device=NPU` -- use `batch-size=1` and `nireq=4` for best NPU utilization.
+- `device=GPU` -- default in the sample code.
+- `device=CPU` -- change `device=GPU` to `device=CPU`.
+- `device=NPU` -- change `device=GPU` to `device=NPU`; use `batch-size=1` and `nireq=4` for best NPU utilization.
 
 ---
 

export_and_quantize.sh

@@ -44,6 +44,15 @@ else
     echo "Already present: test.jpg"
 fi
 
+echo "--- Downloading sample test video ---"
+if [[ ! -f test_video.mp4 ]]; then
+    wget -q -O test_video.mp4 \
+        https://github.com/intel-iot-devkit/sample-videos/raw/master/people-detection.mp4
+    echo "Downloaded: test_video.mp4"
+else
+    echo "Already present: test_video.mp4"
+fi
+
 if [[ "${PRECISION}" == "FP32" ]]; then
     HALF_FLAG="False"
     EXPORT_LABEL="FP32"